ABSTRACT
Harvesting vibration energy using a triboelectric nanogenerator (TENG) is a promising approach in solving the power supply restriction of the Internet of Things. Currently, the low durability due to friction surface wearing is the primary limitation of TENGs, which restricts their applicability and practicability. This study introduces a non-contact-type TENG aimed at significantly enhancing its durability by increasing its anti-wear capability. The configuration of the proposed TENG includes permanent magnets and rolling-balls. The reciprocating motion of functional friction surfaces, facilitated by the permanent magnets, enhances the efficiency of harvesting low-frequency vibration energy. The embedded rolling-balls are utilized to separate two functional friction surfaces, which minimizes the friction surface wearing between different dielectric materials. The electrical output characteristics of this non-contact TENG under variable load resistances are explored according to sinusoidal excitation based on either variable frequencies or accelerations. The results demonstrate that the proposed nanogenerator can generate a short-circuit current of 2118.2 nA and achieve a peak power density of 9.891 mW/m2. The electrical responses of this non-contact TENG remain stable over 120 000 continuous working cycles, lasting for more than 200 min. Furthermore, the nanogenerator can identify and harvest energy from running or jumping motions performed by individuals in different postures and at various speeds or heights. With its exceptional durability and stability, this non-contact nanogenerator offers a novel approach to low-frequency vibration energy harvesting, paving the way for practical applications in the field.
ABSTRACT
Grip strength is a biomarker of frailty and an evaluation indicator of brain health, cardiovascular morbidity, and psychological health. Yet, the development of a reliable, interactive, and point-of-care device for comprehensive multi-sensing of hand grip status is challenging. Here, a relation between soft buckling metamaterial deformations and built piezoelectric voltage signals is uncovered to achieve multiple sensing of maximal grip force, grip speed, grip impulse, and endurance indicators. A metamaterial computational sensor design is established by hyperelastic model that governs the mechanical characterization, machine learning models for computational sensing, and graphical user interface to provide visual cues. A exemplify grip measurement for left and right hands of seven elderly campus workers is conducted. By taking indicators of grip status as input parameters, human-computer interactive games are incorporated into the computational sensor to improve the user compliance with measurement protocols. Two elderly female schizophrenic patients are participated in the real-time interactive point-of-care grip assessment and training for potentially sarcopenia screening. The attractive features of this advanced intelligent metamaterial computational sensing system are crucial to establish a point-of-care biomechanical platform and advancing the human-computer interactive healthcare, ultimately contributing to a global health ecosystem.
Subject(s)
Hand Strength , Sarcopenia , Humans , Female , Aged , Ecosystem , Point-of-Care Systems , ComputersABSTRACT
Recent advancements in reprogrammable metamaterials have enabled the development of intelligent matters with variable special properties in situ. These metamaterials employ intra-element physical reconfiguration and inter-element structural transformation. However, existing mono-characteristic homo-element mechanical metamaterials have limited reprogramming functions. Here, we introduce a reprogrammable mechanical metamaterial composed of origami elements with heterogeneous mechanical properties, which achieves various mechanical behavior patterns by functional group transformations and ring reconfigurations. Through the anisotropic assembly of two heterogeneous elements into a functional group, we enable mechanical behavior switching between positive and negative stiffness. The resulting polygonal ring exhibits rotational deformation, zero Poisson's ratio stretching/compression deformation, and negative Poisson's ratio auxetic deformation. Arranging these rings periodically yields homogeneous metamaterials. The reconfiguration of quadrilateral rings allows for continuous fine-tunability of the mechanical response and negative Poisson's ratio. This mechanical metamaterial could provide a versatile material platform for reprogrammable mechanical computing, multi-purpose robots, transformable vehicles and architectures at different scales.
ABSTRACT
Owing to their compliance, soft robots demonstrate enhanced compatibility with humans and the environment compared with traditional rigid robots. However, ensuring the working effectiveness of artificial muscles that actuate soft robots in confined spaces or under loaded conditions remains a challenge. Drawing inspiration from avian pneumatic bones, we propose the incorporation of a lightweight endoskeleton into artificial muscles to augment the mechanical integrity and tackle load-bearing environmental difficulties. We present a soft origami hybrid artificial muscle that features a hollow origami metamaterial interior with a rolled dielectric elastomer exterior. The programmable nonlinear origami metamaterial endoskeleton significantly improves the blocked force and load-bearing capability of the dielectric elastomer artificial muscle and an increased actuation strain. The origami hybrid artificial muscle demonstrates a maximum strain of 8.5% and a maximum actuating stress of 12.2 mN mm-2 at 30 V µm-1 while preserving its actuating ability, even under a 450 mN load, which is equivalent to 155 times its own weight. We further investigate the dynamic responses and demonstrate the potential use of the hybrid artificial muscle in flapping-wing actuation applications.
